# Check Joint Binary Probabilities

### Description

The main diagonal elements `commonprob[i,i]`

are interpreted as
probabilities *p(A_i)* that a binary variable *A_i*
equals 1. The
off-diagonal elements `commonprob[i,j]`

are the probabilities
*p(A_iA_j)* that both *A_i* and *A_j* are 1.

This programs checks some necessary conditions on these probabilities
which must be fulfilled in order that a joint distribution of the
*A_i* with the given probabilities can exist.

The conditions checked are

*0 <= p(A_i) <= 1*

*
max(0, p(A_i)+p(A_j)-1) <= p(A_iA_j) <= min(p(A_i), p(A_j)), i != j*

*
p(A_i)+p(A_j)+p(A_k)-p(A_iA_j)-p(A_iA_k)-p(A_jA_k) <= 1,
i != j, i != k, j != k*

### Usage

1 | ```
check.commonprob(commonprob)
``` |

### Arguments

`commonprob` |
Matrix of pairwise probabilities. |

### Value

`check.commonprob`

returns `TRUE`

, if all conditions are
fulfilled. The attribute `"message"`

of the return value contains
some information on the errors that were found.

### Author(s)

Andreas Weingessel

### References

Friedrich Leisch, Andreas Weingessel and Kurt Hornik (1998). On the generation of correlated artificial binary data. Working Paper Series, SFB “Adaptive Information Systems and Modelling in Economics and Management Science”, Vienna University of Economics, http://www.wu-wien.ac.at/am

### See Also

`simul.commonprob`

,
`commonprob2sigma`

### Examples

1 2 3 4 5 | ```
check.commonprob(cbind(c(0.5, 0.4), c(0.4, 0.8)))
check.commonprob(cbind(c(0.5, 0.25), c(0.25, 0.8)))
check.commonprob(cbind(c(0.5, 0, 0), c(0, 0.5, 0), c(0, 0, 0.5)))
``` |

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